Method and apparatus for labeling in steered visual analysis of collections of documents

• US 20060218140A1
• Filed: 11/03/2005
• Published: 09/28/2006
• Est. Priority Date: 02/09/2005
• Status: Abandoned Application

• Current Assignee: Battelle Memorial Institute
• Original Assignee: Battelle Memorial Institute
• Inventors: Whitney, Paul D., Havre, Susan L., McGee, David R.
• Application Number:
  US11/268,282
• Publication Number:
  US 20060218140A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/3347 using vector based model
• CPC Class Codes: G06F 16/338 Presentation of query results

Abstract

A method of labeling in steered visual analysis of a collection of documents, the method comprising receiving a query against a database including a collection of documents; representing contents of the query as a matrix; rotating document vectors associated with respective documents to match the matrix to produce a matrix of rotated document vectors; grouping the rotated document vectors into clusters; and displaying a graphic around an area corresponding to a query term.

Claims

1. A method of labeling in steered visual analysis of a collection of documents, the method comprising:

receiving a query against a database including a collection of documents;

representing contents of the query as a matrix;

rotating document vectors associated with respective documents to match the matrix to produce a matrix of rotated document vectors;

grouping the rotated document vectors into clusters; and

displaying a graphic around an area corresponding to a query term.

2. A method in accordance with claim 1 wherein the graphic comprises an ellipse.

3. A method in accordance with claim 1 wherein the graphic comprises a user selectable graphic selected from a plurality of available graphics.

4. A method in accordance with claim 1 and further comprising labeling the clusters.

5. A method in accordance with claim 2 and further comprising providing a label proximate the ellipse.

6. A method in accordance with claim 4 wherein the labeling comprises applying a label corresponding to a term included in the query.

7. A computer readable medium bearing computer program code which, when loaded in a computer, causes the computer to:

receive a query against a database including a collection of documents;

represent contents of the query as a matrix;

rotate document vectors associated with respective documents to match the matrix to produce a matrix of rotated document vectors;

group the rotated document vectors into clusters; and

display a graphic around an area corresponding to a query term.

8. A computer readable medium in accordance with claim 7 wherein the graphic comprises an ellipse.

9. A computer readable medium in accordance with claim 7 wherein the graphic comprises a user selectable graphic selected from a plurality of available graphics.

10. A computer readable medium in accordance with claim 7 and further comprising labeling the clusters.

11. A computer readable medium in accordance with claim 8 and further comprising providing a label proximate the ellipse.

12. A computer readable medium in accordance with claim 10 wherein the labeling comprises applying a label corresponding to a term included in the query.

13. A method comprising:

semantically filtering a set of documents in a database to extract a set of semantic concepts, to improve an efficiency of a predictive relationship to its content, based on at least one of word frequency, overlap and topicality;

defining a topic set, the topic set being characterized as the set of semantic concepts which best discriminate the content of the documents containing them, the topic set being defined based on at least one of word frequency, overlap and topicality;

forming a matrix with the semantic concepts contained within the topic set defining one dimension of the matrix and the semantic concepts contained within the filtered set of documents comprising another dimension of the matrix;

calculating matrix entries as the conditional probability that a document in the database will contain each semantic concept in the topic set given that it contains each semantic concept in the filtered set of documents;

providing the matrix entries as document vectors to interpret the document contents of the database;

inputting query terms;

augmenting the topic set by the query terms;

making an incidence matrix of query terms for the documents;

rotating the document vectors to match the incidence matrix;

clustering and projecting the rotated document vectors; and

displaying a graphic around a cluster and labeling the graphic with a query term related to the cluster.

14. A method in accordance with claim 13 wherein the graphic comprises an ellipse.

15. A method in accordance with claim 14 wherein the graphic comprises a user selectable graphic selected from a plurality of available graphics.

16. A method in accordance with claim 13 wherein the labeling comprises displaying the query term proximate the ellipse.

17. A computer readable medium bearing computer program code which, when loaded in a computer, causes the computer to:

semantically filter a set of documents in a database to extract a set of semantic concepts, to improve an efficiency of a predictive relationship to its content, based on at least one of word frequency, overlap and topicality;

define a topic set, the topic set being characterized as the set of semantic concepts which best discriminate the content of the documents containing them, the topic set being defined based on at least one of word frequency, overlap and topicality;

form a matrix with the semantic concepts contained within the topic set defining one dimension of the matrix and the semantic concepts contained within the filtered set of documents comprising another dimension of the matrix;

calculate matrix entries as the conditional probability that a document in the database will contain each semantic concept in the topic set given that it contains each semantic concept in the filtered set of documents;

provide the matrix entries as document vectors to interpret the document contents of the database;

input query terms;

augment the topic set by the query terms;

make an incidence matrix of query terms for the documents;

rotate the document vectors to match the incidence matrix;

cluster and project the rotated document vectors; and

display a graphic around a cluster and labeling the graphic with a query term related to the cluster.

18. A computer readable medium with claim 17 wherein the graphic comprises an ellipse.

19. A computer readable medium in accordance with claim 18 wherein the graphic comprises a user selectable graphic selected from a plurality of available graphics.

20. A computer readable medium in accordance with claim 17 wherein the labeling comprises displaying the query term proximate the ellipse.

21. A method comprising:

semantically filtering a set of documents in a database to extract a set of semantic concepts, to improve an efficiency of a predictive relationship to its content, based on at least one of word frequency, overlap and topicality;

defining a topic set, the topic set being characterized as the set of semantic concepts which best discriminate the content of the documents containing them, the topic set being defined based on at least one of word frequency, overlap and topicality;

forming a matrix with the semantic concepts contained within the topic set defining one dimension of the matrix and the semantic concepts contained within the filtered set of documents comprising another dimension of the matrix;

calculating matrix entries as the conditional probability that a document in the database will contain each semantic concept in the topic set given that it contains each semantic concept in the filtered set of documents;

providing the matrix entries as document vectors to interpret the document contents of the database;

inputting query terms;

augmenting the topic set by the query terms;

making an incidence matrix of query terms for the documents;

rotating the document vectors to match the incidence matrix;

clustering and projecting the rotated document vectors;

displaying labels for clusters; and

providing a user interface using which a user can adjust the influence of query terms in the labels.

22. A method in accordance with claim 21 wherein the user interface is a graphical user interface.

23. A method in accordance with claim 22 wherein the graphical user interface comprises a slider.

24. A method in accordance with claim 22 wherein the graphical user interface comprises a slider which is actuable using a mouse.

25. A computer readable medium bearing computer program code which, when loaded in a computer, causes the computer to:

semantically filter a set of documents in a database to extract a set of semantic concepts, to improve an efficiency of a predictive relationship to its content, based on at least one of word frequency, overlap and topicality;

define a topic set, the topic set being characterized as the set of semantic concepts which best discriminate the content of the documents containing them, the topic set being defined based on at least one of word frequency, overlap and topicality;

form a matrix with the semantic concepts contained within the topic set defining one dimension of the matrix and the semantic concepts contained within the filtered set of documents comprising another dimension of the matrix;

calculate matrix entries as the conditional probability that a document in the database will contain each semantic concept in the topic set given that it contains each semantic concept in the filtered set of documents;

provide the matrix entries as document vectors to interpret the document contents of the database;

input query terms;

augment the topic set by the query terms;

make an incidence matrix of query terms for the documents;

rotate the document vectors to match the incidence matrix;

cluster and project the rotated document vectors;

display labels for clusters; and

provide a user interface using which a user can adjust the influence of query terms in the labels.

26. A computer readable medium in accordance with claim 25 wherein the user interface is a graphical user interface.

27. A computer readable medium in accordance with claim 25 wherein the graphical user interface comprises a slider.

28. A computer readable medium in accordance with claim 25 wherein the graphical user interface comprises a slider which is actuable using a mouse.